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LSM733-PRODUCTION OPERATIONS MANAGEMENT

By: OSMAN BIN SAIF

LECTURE 5

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Summary of last Session

• Case Study– Hard Rock Cafe• Projects Why?• Characteristics and Activities• Work Break down structure• Project Scheduling techniques• Cost time trade offs• Project Control reports

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Agenda for this Session• What Is Forecasting?• Forecasting Time Horizons• The Strategic Importance of Forecasting• Forecasting Approaches– Qualitative Methods– Quantitative Methods

• Associative Forecasting Methods: Regression and Correlation Analysis

• Monitoring and Controlling Forecasts• Focus Forecasting• Forecasting in the Service Sector

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What is Forecasting? Process of predicting a

future event Underlying basis

of all business decisions Production Inventory Personnel Facilities

??

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The Realities!

Forecasts are seldom perfect Most techniques assume an

underlying stability in the system Product family and aggregated

forecasts are more accurate than individual product forecasts

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Short-range forecast Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, job

assignments, production levels Medium-range forecast

3 months to 3 years Sales and production planning, budgeting

Long-range forecast 3+ years New product planning, facility location, research

and development

Forecasting Time Horizons

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Influence of Product Life Cycle

Introduction and growth require longer forecasts than maturity and decline

As product passes through life cycle, forecasts are useful in projecting Staffing levels Inventory levels Factory capacity

Introduction – Growth – Maturity – Decline

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Product Life Cycle

Product design and development criticalFrequent product and process design changesShort production runsHigh production costsLimited modelsAttention to quality

Introduction Growth Maturity Decline

OM

Str

ateg

y/Is

sues

Forecasting criticalProduct and process reliabilityCompetitive product improvements and optionsIncrease capacityShift toward product focusEnhance distribution

StandardizationFewer product changes, more minor changesOptimum capacityIncreasing stability of processLong production runsProduct improvement and cost cutting

Little product differentiationCost minimizationOvercapacity in the industryPrune line to eliminate items not returning good marginReduce capacity

Figure 2.5

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Forecasting Approaches

Used when little data exist New products New technology

Involves intuition, experience e.g., forecasting sales on Internet

Qualitative Methods

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Qualitative Methods

1. Jury of executive opinion (Pool opinions of high-level experts, sometimes augment by statistical models)

2. Delphi method (Panel of experts, queried iteratively until consensus is reached)

3. Sales force composite (Estimates from individual salespersons are reviewed for reasonableness, then aggregated)

4. Consumer Market Survey (Ask the customer)

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Quantitative Approaches

Used when situation is ‘stable’ and historical data exist Existing products Current technology

Involves mathematical techniques e.g., forecasting sales of LCD televisions

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Quantitative Approaches

1. Naive approach2. Moving averages3. Exponential

smoothing4. Trend projection5. Linear regression

time-series models

associative model

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Set of evenly spaced numerical data Obtained by observing response variable at

regular time periods Forecast based only on past values, no

other variables important Assumes that factors influencing past and

present will continue influence in future

Time Series Forecasting

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Trend

Seasonal

Cyclical

Random

Time Series Components

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Components of DemandD

eman

d fo

r pro

duct

or s

ervi

ce

| | | |1 2 3 4

Time (years)

Average demand over 4 years

Trend component

Actual demand line

Random variation

Figure 4.1

Seasonal peaks

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Persistent, overall upward or downward pattern

Changes due to population, technology, age, culture, etc.

Typically several years duration

Trend Component

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Regular pattern of up and down fluctuations

Due to weather, customs, etc. Occurs within a single year

Seasonal Component

Number ofPeriod Length Seasons

Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12Year Week 52

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Repeating up and down movements Affected by business cycle, political, and

economic factors Multiple years duration

Cyclical Component

0 5 10 15 20

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Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen

events Short duration

and nonrepeating

Random Component

M T W T F

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Naive Approach Assumes demand in next

period is the same as demand in most recent period e.g., If January sales were 68, then

February sales will be 68 Sometimes cost effective and efficient Can be good starting point

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MA is a series of arithmetic means Used if little or no trend

Used often for smoothing Provides overall impression of data over

time

Moving Average Method

Moving average =∑ demand in previous n periods

n

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January 10February 12March 13April 16May 19June 23July 26

Actual 3-MonthMonth Shed Sales Moving Average

(12 + 13 + 16)/3 = 13 2/3

(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 1/3

Moving Average Example

101213

(10 + 12 + 13)/3 = 11 2/3

Graph of Moving Average

| | | | | | | | | | | |J F M A M J J A S O N D

Shed

Sal

es

30 –28 –26 –24 –22 –20 –18 –16 –14 –12 –10 –

Actual Sales

Moving Average Forecast

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Used when some trend might be present Older data usually less important

Weights based on experience and intuition

Weighted Moving Average

Weightedmoving average =

∑ (weight for period n) x (demand in period n) ∑ weights

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January 10February 12March 13April 16May 19June 23July 26

Actual 3-Month WeightedMonth Shed Sales Moving Average

[(3 x 16) + (2 x 13) + (12)]/6 = 141/3

[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 201/2

Weighted Moving Average

101213

[(3 x 13) + (2 x 12) + (10)]/6 = 121/6

Weights Applied Period3 Last month2 Two months ago1 Three months ago6 Sum of weights

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Increasing n smooths the forecast but makes it less sensitive to changes

Do not forecast trends well Require extensive historical data

Potential Problems With Moving Average

Moving Average And Weighted Moving Average

30 –

25 –

20 –

15 –

10 –

5 –

Sale

s de

man

d

| | | | | | | | | | | |J F M A M J J A S O N D

Actual sales

Moving average

Weighted moving average

Figure 4.2

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Seasonal Variations In Data

The multiplicative seasonal model can adjust trend data for seasonal variations in demand (jet skis, snow mobiles)

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Seasonal Variations In Data

1. Find average historical demand for each season 2. Compute the average demand over all seasons 3. Compute a seasonal index for each season 4. Estimate next year’s total demand5. Divide this estimate of total demand by the number of seasons, then

multiply it by the seasonal index for that season

Steps in the process:

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Seasonal Index Example

Jan 80 85 105 90 94Feb 70 85 85 80 94Mar 80 93 82 85 94Apr 90 95 115 100 94May 113 125 131 123 94Jun 110 115 120 115 94Jul 100 102 113 105 94Aug 88 102 110 100 94Sept 85 90 95 90 94Oct 77 78 85 80 94Nov 75 72 83 80 94Dec 82 78 80 80 94

Demand Average Average Seasonal Month 2010 2011 2012 2010-2012 Monthly Index

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Seasonal Index Example

Jan 80 85 105 90 94Feb 70 85 85 80 94Mar 80 93 82 85 94Apr 90 95 115 100 94May 113 125 131 123 94Jun 110 115 120 115 94Jul 100 102 113 105 94Aug 88 102 110 100 94Sept 85 90 95 90 94Oct 77 78 85 80 94Nov 75 72 83 80 94Dec 82 78 80 80 94

Demand Average Average Seasonal Month 2010 2011 2012 2010-2012 Monthly Index

0.957

Seasonal index = Average 2010-2012 monthly demandAverage monthly demand

= 90/94 = .957

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Seasonal Index Example

Jan 80 85 105 90 94 0.957Feb 70 85 85 80 94 0.851Mar 80 93 82 85 94 0.904Apr 90 95 115 100 94 1.064May 113 125 131 123 94 1.309Jun 110 115 120 115 94 1.223Jul 100 102 113 105 94 1.117Aug 88 102 110 100 94 1.064Sept 85 90 95 90 94 0.957Oct 77 78 85 80 94 0.851Nov 75 72 83 80 94 0.851Dec 82 78 80 80 94 0.851

Demand Average Average Seasonal Month 2010 2011 2012 2010-2012 Monthly Index

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Seasonal Index Example

Jan 80 85 105 90 94 0.957Feb 70 85 85 80 94 0.851Mar 80 93 82 85 94 0.904Apr 90 95 115 100 94 1.064May 113 125 131 123 94 1.309Jun 110 115 120 115 94 1.223Jul 100 102 113 105 94 1.117Aug 88 102 110 100 94 1.064Sept 85 90 95 90 94 0.957Oct 77 78 85 80 94 0.851Nov 75 72 83 80 94 0.851Dec 82 78 80 80 94 0.851

Demand Average Average Seasonal Month 2010 2011 2012 2010-2012Monthly Index

Expected annual demand = 1,200

Jan x .957 = 961,20012

Feb x .851 = 851,20012

Forecast for 2013

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Seasonal Index Example

140 –

130 –

120 –

110 –

100 –

90 –

80 –

70 –| | | | | | | | | | | |J F M A M J J A S O N D

Time

Dem

and

2013 Forecast2012 Demand 2011 Demand2010 Demand

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Associative Forecasting

Used when changes in one or more independent variables can be used to predict the changes in the dependent variable

Most common technique is linear regression analysis

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Associative ForecastingForecasting an outcome based on predictor variables using the least squares technique

y = a + bx^

where y = computed value of the variable to be predicted (dependent variable)a = y-axis interceptb = slope of the regression linex = the independent variable though to predict the value of the dependent variable

^

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Associative Forecasting ExampleSales Area Payroll

($ millions), y ($ billions), x2.0 13.0 32.5 42.0 22.0 13.5 7

4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sale

s

Area payroll

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Associative Forecasting Example

Sales, y Payroll, x x2 xy

2.0 1 1 2.03.0 3 9 9.02.5 4 16 10.02.0 2 4 4.02.0 1 1 2.03.5 7 49 24.5

∑y = 15.0 ∑x = 18 ∑x2 = 80 ∑xy = 51.5

x = ∑x/6 = 18/6 = 3

y = ∑y/6 = 15/6 = 2.5

b = = = .25∑xy - nxy∑x2 - nx2

51.5 - (6)(3)(2.5)80 - (6)(32)

a = y - bx = 2.5 - (.25)(3) = 1.75

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Associative Forecasting Example

y = 1.75 + .25x^ Sales = 1.75 + .25(payroll)

If payroll next year is estimated to be $6 billion, then:

Sales = 1.75 + .25(6)Sales = $3,250,000

4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Nod

el’s

sale

s

Area payroll

3.25

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How strong is the linear relationship between the variables?

Correlation does not necessarily imply causality!

Coefficient of correlation, r, measures degree of association Values range from -1 to +1

Correlation

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Correlation Coefficient

r = nSxy - SxSy

[nSx2 - (Sx)2][nSy2 - (Sy)2]

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Correlation Coefficient

r = nSxy - SxSy

[nSx2 - (Sx)2][nSy2 - (Sy)2]

y

x(a) Perfect positive correlation: r = +1

y

x(b) Positive correlation: 0 < r < 1

y

x(c) No correlation: r = 0

y

x(d) Perfect negative correlation: r = -1

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Coefficient of Determination, r2, measures the percent of change in y predicted by the change in x Values range from 0 to 1 Easy to interpret

Correlation

For the Nodel Construction example:r = .901r2 = .81

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Multiple Regression Analysis

If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variables

y = a + b1x1 + b2x2 …^

Computationally, this is quite complex and generally done on the computer

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Multiple Regression Analysis

y = 1.80 + .30x1 - 5.0x2

^

In the Nodel example, including interest rates in the model gives the new equation:

An improved correlation coefficient of r = .96 means this model does a better job of predicting the change in construction sales

Sales = 1.80 + .30(6) - 5.0(.12) = 3.00Sales = $3,000,000

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Measures how well the forecast is predicting actual values

Ratio of cumulative forecast errors to MEAN ABSOLUTE DEVIATION (MAD) Good tracking signal has low values If forecasts are continually high or low, the forecast

has a bias error

Monitoring and Controlling Forecasts

Tracking Signal

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Monitoring and Controlling Forecasts

Tracking signal

Cumulative errorMAD=

Tracking signal =

∑(Actual demand in period i - Forecast demand in period i)(∑|Actual - Forecast|/n)

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Tracking Signal

Tracking signal

+

0 MADs

–

Upper control limit

Lower control limit

Time

Signal exceeding limit

Acceptable range

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Tracking Signal ExampleCumulative

Absolute AbsoluteActual Forecast Cumm Forecast Forecast

Qtr Demand Demand Error Error Error Error MAD

1 90 100 -10 -10 10 10 10.02 95 100 -5 -15 5 15 7.53 115 100 +15 0 15 30 10.04 100 110 -10 -10 10 40 10.05 125 110 +15 +5 15 55 11.06 140 110 +30 +35 30 85 14.2

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CumulativeAbsolute Absolute

Actual Forecast Cumm Forecast ForecastQtr Demand Demand Error Error Error Error MAD

1 90 100 -10 -10 10 10 10.02 95 100 -5 -15 5 15 7.53 115 100 +15 0 15 30 10.04 100 110 -10 -10 10 40 10.05 125 110 +15 +5 15 55 11.06 140 110 +30 +35 30 85 14.2

Tracking Signal Example

TrackingSignal(Cumm Error/MAD)

-10/10 = -1-15/7.5 = -20/10 = 0-10/10 = -1+5/11 = +0.5+35/14.2 = +2.5

The variation of the tracking signal between -2.0 and +2.5 is within acceptable limits

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Forecasting in the Service Sector

Presents unusual challenges Special need for short term records Needs differ greatly as function of

industry and product Holidays and other calendar events Unusual events

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Summary of this Session• What Is Forecasting?• Forecasting Time Horizons• The Strategic Importance of Forecasting• Forecasting Approaches– Qualitative Methods– Quantitative Methods

• Associative Forecasting Methods: Regression and Correlation Analysis

• Monitoring and Controlling Forecasts• Focus Forecasting• Forecasting in the Service Sector

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THANK YOU